function [vL,vR]=RFNN_EKF_cal_v2(dg,do,sig,sio,X,Y,xo,yo,Xd,Yd)

    %coder.inline("never");

    %输入向量
%     aph=0.3;
    b=0.098;
    S=[dg,do,sig,sio];
    a=[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1;
        1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1];
%     a=[-0.35,0.21,0.11,0.25,-0.33,0.42,0.18,0.06,-0.11,-0.15,-0.23,0.26,0.36,-0.07,0.13,0.02;
%         2.47,-1.63,0.11,-3.94,0.45,0.82,0.35,-2.27,2.61,-0.84,7.19,-0.84,2.62,-3.9,6.76,-0.2];
    %神经网络部分计算输出
    W=a*sum(S(:));
    [Outputs,Fnk]=RFNNpro_v(S,W);
    v=Outputs(1);
    w=Outputs(2);
    vL=v-w*b;
    vR=v+w*b;
%     T=[cos(car_angle),sin(car_angle),-X*cos(car_angle)-Y*sin(car_angle);
%                 -sin(car_angle),cos(car_angle),X*sin(car_angle)-Y*cos(car_angle);
%                 0,0,1];
    
end